49. The Value of Data, Part 2 (Leo Polovets)

The Full Ratchet Podcast on iTunesNick Moran Angel List

Leo Polovets of Susa Ventures joins Nick to cover The Value of Data, Part Two. We will address questions including:

  • Polovets Value of Data part 2Transitioning to business models and the way data can be wielded to generate value. Can you provide an example of a business that relies on data within their business model to create this symbiotic, accretive value generation?
  • You’ve cited three ways in which data can be monetized. Can you highlight each?
  • I want to touch on HOW companies can put this into practice… including content companies, ecommerce, data providers, and tools. Let’s start out w/ content companies. How might an early-stage company in the Content space put together a data game-plan?
  • How about an ecommerce company? What’s an example of an ecommerce startup that has executed a strong monetization game plan?
  • How about data providers? What’s their model for generating revenue and who are their customers?
  • Finally, on the B2B or B2C tools-side, can you talk through an exmaple, let’s say a B2B SaaS company that effectively monetizes data?
  • How should data-centric companies think about their pricing models and can the pricing model change for the same product that has different value to different types of customers?
  • Taking a step back, as an early-stage investor, what advice would have when thinking about data and investing in companies that are either focusing on data or have an element of their strategy where valuable data is being acquired?
  • As a startup founder that projects to have a business where the data is the competitive advantage and creates the key value…. what thoughts come to mind in terms of crossing the data chasm in order to get the necessary traction early without a data advantage in order to reach a point where a critical mass of data exists to realize that competitive advantage?

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Key Takeaways:
 
 
For today’s takeaways, I wanted to review the three major sections and their sub-components within the value of data series.
 
1- The Importance of Data
 
Here we discussed the historical reasons why data is becoming more important and also the ways data has become a strong, defensible, sustainable competitive advantage.  Older forms of competitive advantages, like software and hardware, are no longer as defensible.
 
What are the types of competitive advantage?  Leo mentioned:
  • Recommendations… where he gave the example of Yelp reviews
  • Improved Efficiency… where Leo mentioned Uber and their ability to optimize their fleet of vehicles to serve the demand in different locations at different times
  • General Predictions and Modeling… where he talked about LendUp where they can make better predictions about what people can and can not pay as opposed to using an older, innaccurate method such as their credit score
 
 
2- Data Collection
 
This section related to collection methods and tips.  Here we reviewed how to tell if the data can be valuable and how to collect it.
 
4 attributes of data
1. It’s hard to build.  Easy to acquire data sets will have less value because they’re not proprietary.
2. It’s clean, accurate and up-to-date.  Bad data and old data does not just have no value, it actually has negative value if it is being used to make decisions.
3. The data is useful.  In this portion Leo compared purchase history and it’s tremendous predictive value vs. data on shoe size, which may have very limited value.
4. The size of the data set.  This relates to sample size and statistical significance but even beyond that large-sized data sets are not only more relevant but can be used in many more ways.
 
5 Major sources of data
1. Direct collection… Asking users for feedback
2. Crowdsourcing… So, where direct collection is often an outbound request, this source typically occurs inbound when a user chooses to contribute, unsolicited.
3. Paid Crowdsourcing… This is different than the previous in that the company has hired an individual or service to acquire data.  In this case the data may be publicly available but not organized the way you need it or may be scattered across many sources.
4. Data Exhaust… This is data collected during normal usage of a product that the user often doesn’t realize.  It could be as simple as clicking one link when a list of five links are presented.  As digital online networks have grown, the importance of Data Exhaust has only grown with it.
5. Combining Data Sets… This is the inter-relationship between data sets that creates insights.
 
 
And Leo closed-off this section by suggesting that startups collect as much data as early as possible.  The analysis and processing of the data is less important early-on, but merely the fact that it has been collected in a clean way will create many opportunities for a competitive advantage down the road.
 
 
  
3- Data Business Models
 
Section 3- Data-centric Business Models.  This is where we reviewed different businesses that use data at their core
 
Monetization Methods:
1. Selling the data directly.  If you have data that others would like access to, the data itself can be the product or service
2.  Increasing Revenue.  This is possible via better recommendations, better ad targeting.  Essentially the better you understand your customers the better you can serve them through products and services.
3.  Expanding Margin.  This has to do with optimizing pricing or optimizing the cost-side of the business.  An example here was holding more appropriate levels of inventory, which can reduce inventory cost and also increase revenue by preventing going out of stock.
 
 
Types of companies:
1. Content Companies: For these types of businesses Leo advocates A/B testing different types of content and different headlines.  He also talked about measuring which types of content may ellicit more sharing and which types may cause more engagement and time reading other related material.  He called this “instrumenting readership.”  Depending on current goals for growth or engagement, different approaches can be used based on the data.
2. eCommerce: Here he cited companies that send out boxes of products and subsequently monitor what people keep, what they send back, what types of products and product characteristics most resonate with them.
3. Data Providers: For this group, Leo talked about those that sell access to premium data, those that sell API access to raw data, and finally those that wish to augment their existing data sets with external data.
4. B2B and B2C Tools:  The final tool that Leo has written about relates to tools.  In this area, Leo’s favorite includes tools that improve efficiency through converting emails or faxes into online forms.  Approaches such as these can be used to collect large amount of organized data by streamlining data entry for users.
 
 
And Leo finished-off the discussion by reminding us that customers don’t come initially for the data advantages, b/c it takes time to build value from data.  So entrepreneurs need to first think about building a value proposition separate from the data in order to acquire customers.  It is only after a critical mass of users and activity that the business can evolve and the experience can be enhanced through the value of data.
 
 

 

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